CNSPulse

STRATEGIES TO IMPROVE TREATMENT EFFECT DETECTION IN MDD TRIALS

George Papakostas — Massachusetts General Hospital

2026 ASCP Annual Meeting

Major depressive disorder (MDD) is a serious and prevalent condition associated with huge patient and family suffering and great socioeconomic loss. Over 50% of patients do not respond to first line antidepressants. Considering the heterogeneity of MDD and the different proposed treatments, response prediction markers may improve treatment effect detection. There is an urgent need to optimize clinical trial methodologies to identify patient characteristics associated with higher probability for response, thereby advancing treatment discovery for MDD. This session will highlight select emerging approaches to enhance treatment-effect detection in MDD trials, including patient subtyping, phenotype enrichment and the use of biomarkers. Optimizing clinical trial methods to detect effective treatments and to understand patient characteristics associated with optimal response is critical to advancing treatment discovery for MDD. This talk will present new data linking brain features to positive outcomes for patients in trials of MDD that utilize novel agents to target the KCNQ channel in the brain. The talk will consider strategies including patient subtyping and phenotype enrichment to move closer to the goal of personalized treatment for depression. The first speaker, Dr. Murrough, will present new data linking specific brain features to positive clinical outcomes in trials evaluating novel KCNQ-channel-targeting agents for MDD. The second speaker, Dr. Guidetti, will provide an overview of evolving definitions and clinical correlates of antidepressant tachyphylaxis, discuss its potential implications for drug development, and present results of a post hoc analysis of Phase 3 trial evaluating an NMDA receptor antagonist as adjunctive treatment in patients with inadequate response to 1-3 first line antidepressants. The final speaker, Dr. Mignot, will discuss the objective assessment of sleep using wearable technology, its value in predicting future disease risk, and its potential applications in the context of depression. Together, these presentations underscore the potential of integrating subgroup analyses, biological and behavioral markers into MDD trials to improve the detection of treatment effects and accelerate progress toward personalized treatment strategies.

Learning Objective 1: By the end of this session, attendees should understand how methodological approaches – such as patient subtyping, phenotype enrichment, and the inclusion of objective biomarkers – can enhance the detection of treatment effects in MDD clinical trials.

Learning Objective 2: By the end of this session, attendees should be able to evaluate the potential of incorporating brain features, subgroups of MDD patients (e.g., patients with antidepressant tachyphylaxis), objective digital measures (e.g., wearable-derived sleep data) to enhance patient characterization and treatment effect detection in MDD clinical trials.

References

  1. Chowdhury A, Boukezzi S, Costi S, Hameed S, Jacob Y, Salas R, Iosifescu DV, Han MH, Swann A, Mathew SJ, Morris L, Murrough JW. Effects of the KCNQ (Kv7) Channel Opener Ezogabine on Resting-State Functional Connectivity of Striatal Brain Reward Regions, Depression, and Anhedonia in Major Depressive Disorder: Results From a Randomized Controlled Trial. Biol Psychiatry. 2025 Oct 1;98(7):568-577. doi: 10.1016/j.biopsych.2025.02.897. Epub 2025 Mar 4. PMID: 40049579; PMCID: PMC12353514.
  2. Guidetti C, Papakostas GI, Pani L, De Martin S, Serra G, Apicella M, Kröger C, Champasa P, Comai S, Mattarei A, Folli F, Pappagallo M, Manfredi PL, Fava M. Esmethadone (REL1017) in Patients With Major Depressive Disorder and Antidepressant Tachyphylaxis: An Exploratory Post Hoc Analysis From a Phase 3 Randomized Controlled Trial. J Clin Psychiatry. 2025 Oct 6;86(4):24m15748. doi: 10.4088/JCP.24m15748. PMID: 41060071.